issues: 1085619598

What happened:

The uint data type variables are read as float64 instead of the correct uint type.

Minimal Complete Verifiable Example:

```python import xarray as xr

print(xr.open_dataset('test_save.nc')['processing_quality_flags'].dtype) ```

Anything else we need to know?:

The sample data is attached here. The output of ncdump -h test_save.nc: netcdf test_save { dimensions: y = 3246 ; x = 450 ; variables: float longitude(y, x) ; longitude:_FillValue = NaNf ; longitude:name = "longitude" ; longitude:standard_name = "longitude" ; longitude:units = "degrees_east" ; float latitude(y, x) ; latitude:_FillValue = NaNf ; latitude:name = "latitude" ; latitude:standard_name = "latitude" ; latitude:units = "degrees_north" ; uint processing_quality_flags(y, x) ; processing_quality_flags:_FillValue = 4294967295U ; processing_quality_flags:comment = "Flags indicating conditions that affect quality of the retrieval." ; processing_quality_flags:end_time = "2019-07-02 05:00:24" ; processing_quality_flags:file_key = "PRODUCT/SUPPORT_DATA/DETAILED_RESULTS/processing_quality_flags" ; processing_quality_flags:file_type = "tropomi_l2" ; processing_quality_flags:flag_masks = 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 255U, 256U, 512U, 1024U, 2048U, 4096U, 8192U, 16384U, 32768U, 65536U, 131072U, 262144U, 524288U, 1048576U, 2097152U, 4194304U, 8388608U, 16777216U, 33554432U, 67108864U, 134217728U, 268435456U, 536870912U ; processing_quality_flags:flag_meanings = "success radiance_missing irradiance_missing input_spectrum_missing reflectance_range_error ler_range_error snr_range_error sza_range_error vza_range_error lut_range_error ozone_range_error wavelength_offset_error initialization_error memory_error assertion_error io_error numerical_error lut_error ISRF_error convergence_error cloud_filter_convergence_error max_iteration_convergence_error aot_lower_boundary_convergence_error other_boundary_convergence_error geolocation_error ch4_noscat_zero_error h2o_noscat_zero_error max_optical_thickness_error aerosol_boundary_error boundary_hit_error chi2_error svd_error dfs_error radiative_transfer_error optimal_estimation_error profile_error cloud_error model_error number_of_input_data_points_too_low_error cloud_pressure_spread_too_low_error cloud_too_low_level_error generic_range_error generic_exception input_spectrum_alignment_error abort_error wrong_input_type_error wavelength_calibration_error coregistration_error slant_column_density_error airmass_factor_error vertical_column_density_error signal_to_noise_ratio_error configuration_error key_error saturation_error max_num_outlier_exceeded_error solar_eclipse_filter cloud_filter altitude_consistency_filter altitude_roughness_filter sun_glint_filter mixed_surface_type_filter snow_ice_filter aai_filter cloud_fraction_fresco_filter aai_scene_albedo_filter small_pixel_radiance_std_filter cloud_fraction_viirs_filter cirrus_reflectance_viirs_filter cf_viirs_swir_ifov_filter cf_viirs_swir_ofova_filter cf_viirs_swir_ofovb_filter cf_viirs_swir_ofovc_filter cf_viirs_nir_ifov_filter cf_viirs_nir_ofova_filter cf_viirs_nir_ofovb_filter cf_viirs_nir_ofovc_filter refl_cirrus_viirs_swir_filter refl_cirrus_viirs_nir_filter diff_refl_cirrus_viirs_filter ch4_noscat_ratio_filter ch4_noscat_ratio_std_filter h2o_noscat_ratio_filter h2o_noscat_ratio_std_filter diff_psurf_fresco_ecmwf_filter psurf_fresco_stdv_filter ocean_filter time_range_filter pixel_or_scanline_index_filter geographic_region_filter input_spectrum_warning wavelength_calibration_warning extrapolation_warning sun_glint_warning south_atlantic_anomaly_warning sun_glint_correction snow_ice_warning cloud_warning AAI_warning pixel_level_input_data_missing data_range_warning low_cloud_fraction_warning altitude_consistency_warning signal_to_noise_ratio_warning deconvolution_warning so2_volcanic_origin_likely_warning so2_volcanic_origin_certain_warning interpolation_warning saturation_warning high_sza_warning cloud_retrieval_warning cloud_inhomogeneity_warning" ; processing_quality_flags:flag_values = 0U, 1U, 2U, 3U, 4U, 5U, 6U, 7U, 8U, 9U, 10U, 11U, 12U, 13U, 14U, 15U, 16U, 17U, 18U, 19U, 20U, 21U, 22U, 23U, 24U, 25U, 26U, 27U, 28U, 29U, 30U, 31U, 32U, 33U, 34U, 35U, 36U, 37U, 38U, 39U, 40U, 41U, 42U, 43U, 44U, 45U, 46U, 47U, 48U, 49U, 50U, 51U, 52U, 53U, 54U, 55U, 64U, 65U, 66U, 67U, 68U, 69U, 70U, 71U, 72U, 73U, 74U, 75U, 76U, 77U, 78U, 79U, 80U, 81U, 82U, 83U, 84U, 85U, 86U, 87U, 88U, 89U, 90U, 91U, 92U, 93U, 94U, 95U, 96U, 97U, 256U, 512U, 1024U, 2048U, 4096U, 8192U, 16384U, 32768U, 65536U, 131072U, 262144U, 524288U, 1048576U, 2097152U, 4194304U, 8388608U, 16777216U, 33554432U, 67108864U, 134217728U, 268435456U, 536870912U ; processing_quality_flags:long_name = "Processing quality flags" ; processing_quality_flags:modifiers = "" ; processing_quality_flags:platform_shortname = "S5P" ; processing_quality_flags:reader = "tropomi_l2" ; processing_quality_flags:sensor = "tropomi" ; processing_quality_flags:start_time = "2019-07-02 03:18:54" ; processing_quality_flags:coordinates = "latitude longitude" ; }

Note that I can't reproduce it using this example: ``` import numpy as np import xarray as xr

da = xr.DataArray(np.array([1,2,3], dtype='uint')).rename('test_array') da.to_netcdf("test.nc", engine='netcdf4') with xr.open_dataset('test.nc') as ds: print(ds['test_array'].dtype)

uint64 ```

Environment: